Autoregressive Integrated Moving Average (ARIMA) is a time series analysis model that can be dated back to 1955. It has been used in many different fields of study to analyze time series and forecast future data points; however, it has not been widely used to forecast daily wastewater influent flow. The objective of this study is to explore the possibility for wastewater treatment plants (WWTPs) to utilize ARIMA for daily influent flow forecasting. To pursue the objective confidently, five stations across North America are used to validate ARIMA’s performance. These stations include Woodward, Niagara, North Davis, and two confidential plants. The results demonstrate that ARIMA models can produce satisfactory daily influent flow forecasts. Considering the results of this study, ARIMA models could provide the operating engineers at both municipal and rural WWTPs with sufficient information to run the stations efficiently and thus, support wastewater management and planning at various levels within a watershed.

Here, to assess the national climate impact of wastewater treatment and inform decarbonization, we assembled a comprehensive greenhouse gas inventory of 15,863 facilities in the contiguous USA. Considering location and treatment configurations, we modelled on-site CH₄, N₂O and CO₂ production and emissions associated with energy, chemical inputs and solids disposal. Using Monte Carlo simulations, we estimated median national emissions at 47 million tonnes of CO₂ equivalent per year, with on-site process CH₄ and N₂O emissions exceeding current government estimates by 41%. Treatment configurations with anaerobic digesters are responsible for 16 million tonnes of CO₂ equivalent per year of fugitive methane, outweighing benefits achieved through on-site electricity generation. Systems designed for nutrient removal have the highest greenhouse gas emissions intensity, attributable to energy requirements and N₂O production, demonstrating current trade-offs between meeting water quality and climate objectives. We analysed key sensitivities and included a geospatial analysis to highlight the scale and distribution of opportunities for reducing life cycle greenhouse gas emissions.